Method and apparatus for internet customer retention

ABSTRACT

A method of dynamically optimizing customer retention for a web marketing site is provided. That method includes specifying a permissible defunct threshold, specifying a range of offers to be included in a set of promotions, determining a probability that a customer will become defunct in a predetermined period of time since the last interaction of that customer with the web site, and providing a promotion to a customer if the probability that the customer will become defunct in the predetermined period of time since the last interaction of that customer with the web site is greater than a predetermined threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.09/804,728, entitled METHOD AND APPARATUS FOR INTERNET CUSTOMERRETENTION, filed on Mar. 13, 2001, which claims priority to U.S.Provisional Patent Application Ser. No. 60/188,890, both of which areincorporated herein in their entirety.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The disclosed invention relates to marketing goods and services over theInternet and, in particular, detecting through controlled experiments arate of loss of customers based on waiting time or purchase frequency.

2. Description of the Background

The Internet is growing at a prodigious rate. According to currentestimates the amount of information transmitted over the Internet isdoubling approximately every 100 days. This makes it likely thatvisitors to websites will encounter congestion and possibly lengthywaiting times to obtain service. Customers who are impatient will balk(leave the website), possibly in favor of a competitive site. It isdesirable, therefore, to detect impatient customers and ensure that theyare served prior to leaving.

Marketing in the Internet is of a very different character thantraditional marketing. Visiting a physical store requires an investmentof time on the part of the customer, and there are costs associated withleaving the stores, including the time to locate an alternative store,travel to the alternative store, and develop a new buyer/sellerrelationship. On the Internet, each of these functions is only a clickaway, and a customer once lost may never be regained. Such traditionalforces as geographic proximity, which draw customer to brick-and-mortarstores, are absent on the Internet.

It is known in queuing at airports, for example, to provide separatequeues for first-class and coach passengers. The reason is to provide abenefit to those who pay higher airfares, not to prevent balking. It isalso known to divide airplane passengers into groups for boarding so therear of the plan can be filled first to simplify loading. Here thereason is not to prevent balking but to optimize the time required toprepare the plane for takeoff.

It is known in the art of Internet marketing to observe when an existingcustomer has failed to make a purchase within a certain period of time,after which it is assumed that the customer has been “lost” or has goneinactive. Efforts are then made to awaken or recapture the customer withpromotions, email or other contact. By that time, however, the customermay be irretrievably gone and may be purchasing regularly from anothersource.

It is not taught or suggested in the prior art to observe the customer'sbehavior dynamically during his period of interaction with the websiteto anticipate a possible defection and take steps to prevent it. Thepresent invention comprises a method and apparatus for detecting andforestalling a defection before it occurs, as opposed to attempting torecapture the customer after he has gone.

In addition, with traditional commerce, customer retention methods aretypically static, with change only occurring with major market changes.This has resulted in part because the costs associated with establishingand printing advertisements and coupons. Furthermore, it is difficult tooffer different promotions to different purchasers in a traditionalsetting in which promotions are published or made publicly available.

However, e-commerce does not have to be so restricted. The introductionof e-commerce on the Internet has made it easier for Internet merchantsto change customer retention methods by simply updating a Web page,email coupon or appropriate database/systems. The costs associated withprinting catalogs and marking goods in a bricks-and-mortar setting aretypically not present in eCommerce. In addition, it is also possible tooffer different promotions to different customers without eithercustomer learning the price that has been offered to the other.

Although it is possible for Internet merchants to update promotionaloffers at any time, typically they have not done so. One reason forsticking to static customer retention strategies is that is the acceptedmarketing strategy. Merchants are accustomed to keeping promotionaloffers static for a significant period of time. Moreover, in some cases,merchants have both brick-and-mortar shops and web shops, and want tokeep prices, promotions and customer other retention methods inalignment. However, the primary reason why Internet merchants do notdynamically adjust customer retention methods with the ever-changingmarketplace is that the merchants do not have the ability to dynamicallydetermine optimal promotional levels.

The Internet is a dynamic marketplace. As e-commerce becomes a dominantforce, the ability to dynamically adjust to and exploit changes in theInternet marketplace becomes critical. An enormous amount of detailed,disaggregate information is being routinely captured during Internettransactions. The ability to gather real-time information ontransactions conducted on the Internet means that Internet merchantscould use the information to dynamically update their websites to takeadvantage of market conditions. In particular, the availability ofreal-time transaction information opens up the possibility of dynamicpricing and marketing.

However, using the information to determine the dynamic, optimal priceor promotional level is problematic. Although a great deal of real-timetransactional information is available, businesses have no currentmethod of analyzing the information in a manner that provides guidanceto dynamically update pricing, marketing, promotions and other keymarket variables.

As enterprises move into high velocity environments in a networkedeconomy, decisions based on data are ever more critical and can beleveraged to affect the bottom line. In this environment, information ishighly valuable but comes with a high discount rate. That is, the valueof the information rapidly depreciates. Current generation dataanalysis, profiling, and data mining methods do not effectively dealwith this type of information, as current methods rely on atime-consuming sequential process of data gathering, analysis,implementation and feed back.

Current systems, including data mining methodologies, are typicallyretrospective, creating a significant lag in analysis time. The dynamicnature of the Internet makes even recent information utilized in thosemethods obsolete.

Some efforts have been made to use computer systems to estimate supplyand demand, to adjust prices to perceived market conditions, or to varyprices based on the identity and purchasing history of the customer.

U.S. Pat. No. 5,752,238 discloses a consumer-driven electronicinformation pricing mechanism including a pricing modulator and pricinginterface contained with a client system. However, in this reference,the customer selects from a menu of pricing options. It does notdisclose or teach a real-time determination of price sensitivities.

U.S. Pat. Nos. 5,822,736 and 5,987,425 disclose a variable marginpricing system and method that generates retail prices based on customerprice sensitivity in which products are grouped into pools from a firstpool for the most price sensitive products to a last pool for the leastprice sensitive products. However, the price sensitivities aredetermined manually by the storekeeper based on his subjectiveimpressions and are not obtained in real-time.

U.S. Pat. No. 5,878,400 discloses a method and apparatus for computing aprice to be offered to an organization based on the identity of theorganization and the product sought, but does not teach or suggestreal-time price determination.

U.S. Pat. No. 5,918,209 discloses a method and system for determiningmarginal values for perishable resources expiring at a future time, suchas an airline seat, hotel room night, or rental car day for use in aperishable resource revenue management system. Data for the perishableresources and composite resources is loaded from the perishable resourcerevenue management system into the marginal value system. The marginalvalues for the perishable resources are determined using a continuousoptimization function using interdependencies among the perishableresources and the composite resources in the internal data structures.However, this reference does not disclose or teach elicitation of pricesensitivities based on measuring customer behavior.

U.S. Pat. No. 5,926,817 discloses a client-server system and method forproviding real-time access to a variety of database systems, oneapplication of which is “dynamic price quoting.” However, the referenceuses this phrase to mean computing a single price to be quoted to acustomer based on information about the user's requirements and datacontained in the supplier's databases. It does not teach or suggestexperimentation to determine marketplace customer price sensitivity.

In general, the prior art teaches that it is useful to attempt tomeasure supply and demand as an aid in determining prices andpromotional levels to retain customers. It is also known to utilizepreviously accumulated facts about a purchaser to influence the price ordiscounted price at which a particular product should be offered to him.However, the applicants are not aware of any prior art in which price,promotional level, and other market sensitivities are measured directlythrough use of controlled real-time experiments.

In view of the foregoing, it can be appreciated that a substantial needexists for a method and system for dynamically determining optimalpromotional levels, promotional timing, and customer retention methodsfor products and services.

SUMMARY OF THE INVENTION

The inability to effectively exploit Internet transaction information isovercome by the method and system of the present invention, which enableInternet businesses to conduct real-time, online experiments on a sampleof transactions and determine marketplace sensitivities. Analysis of theresults of the experiments reveal optimal values of key market decisionvariables such as price, content of banner ads, promotion levels,quantity discount schemes, etc. The experiments may be automaticallyconducted on an on-going basis, or may be conducted on a periodic basis.The resulting optimal values may also be implemented automatically. Thesystem offers total flexibility to the users to conduct and control theexperiments. The experimental process is based upon rigorous statisticaland econometric principles.

A manager using the method and system of the present invention cancontrol the extent and speed with which market strategies are updated.The method and system of the present invention preferably allowsmanagers to modify the nature of the experiment and the propagation ofoptimal values. Managers make the key business decisions, which aresilently and seamlessly translated into the Internet merchant'seCommerce system.

For example, the profit-maximizing promotional offer determined from thedynamic experiment conducted by the system of the present inventionmight be 5% higher than the currently offered promotional offer. Themanager might use this information for purely diagnostic purposes andthus gain insights into the price sensitivity of the market.Alternatively, the manager might automate the process of changing theoffered promotion to the optimum promotion amount determined by thesystem of the present invention whenever the optimal profit for thepromotion, for example, is 20% or more than current settings. In yetanother embodiment, optimum promotional amounts may be may be constantlyupdated and implemented as the optimal amount changes. Thus, the methodand system of the current invention can be used for a pure diagnosticpurpose or to automate the setting of key market variables.

The dynamic experimentation used by the inventive system reveals therelative stability (or instability) of the networked market within whichthe business operates. The translation of an optimal value for a keyvariable (for example, promotional level or timing) to the entire marketcan be done on a real-time basis.

Continuous real-time modeling with appropriate integration to existingsystems on critical factors like price, promotion, financing, content,discount schemes and product bundling give companies using the methodand system of the present invention a huge competitive advantage.

The present invention includes a method of dynamically optimizingcustomer retention for a web marketing site is provided. That methodincludes specifying a permissible defunct threshold, specifying a rangeof offers to be included in a set of promotions, determining aprobability that a customer will become defunct in a predeterminedperiod of time since the last interaction of that customer with the website, and providing a promotion to a customer if the probability thatthe customer will become defunct in the predetermined period of timesince the last interaction of that customer with the web site is greaterthan a predetermined threshold.

With these and other advantages and features of the invention that willbecome apparent hereinafter, the nature of the invention may be moreclearly understood by reference to the following detailed description ofthe invention, to the appended claims and to the several drawingsattached herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are included to provide a furtherunderstanding of the invention and are incorporated in and constitute apart of this specification, illustrate embodiments of the invention andtogether with the description serve to explain the principles of theinvention.

FIG. 1 is a diagram illustrating the relationship between the samplingengine of the present invention and various applications that use thesampling engine;

FIG. 2 illustrates one embodiment of a system architecture that may beused by the method and system of the present invention;

FIG. 3 illustrates one embodiment of a software system data flow in themethod and system of the present invention; and

FIG. 4 is a flowchart illustrating the process used to perform dynamiccustomer retention by the method and system of the present invention.

DETAILED DESCRIPTION

Reference will now be made in detail to the embodiments of theinvention, examples of which are illustrated in the accompanyingdrawings. Wherever possible, the same reference numbers will be usedthroughout the drawings to refer to the same or like components.

It is worth noting that any reference in the specification to “oneembodiment” or “an embodiment” means that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the invention. The appearancesof the phrase “in one embodiment” in various places in the specificationare not necessarily all referring to the same embodiment.

FIG. 2 illustrates one embodiment of a system architecture for thesystem of the present invention. In this embodiment, a potentialcustomer visits a website run by an Internet merchant and conductseCommerce by purchasing one or more products from the Internet merchantthrough the website.

In the embodiment shown, the customer uses an Internet browser 201 onhis computer to access an eCommerce site operated by an Internetmerchant. The Internet browser 201 may be any known to those skilled inthe art, such as Microsoft Explorer or Netscape Communicator, forexample. Preferably, HyperText Transfer Protocol (HTTP) or its moresecure version HTTPS is used to communicate with the website. These arepopular communication protocols used on the Internet to exchangeinformation. Other communication protocols are known to those skilled inthe art, and are intended to come within the scope of the presentinvention.

In an alternative embodiment not shown, the customer may be using awireless handheld device to access the website.

Once the customer has accessed the eCommerce website, he can requestinformation, such as current prices, from the website. The request sentby the browser might include information specific to the customer usingthe browser. Such information may include, for example, informationderived from user logins, cookies stored on the user's machine andthrough the user's IP address.

The customer's browser 201 communicates with an Internet merchant'seCommerce system 205. The eCommerce system 205 is an integrated systemthat comprises different kinds of hardware and software sub-systems. TheeCommerce system performs the functions needed to run the Internetmerchant's Website.

Webservers are usually the entry point into an eCommerce system 205 fromthe perspective of a software program. The Webserver 210 on theeCommerce system is mainly responsible for delivering webpages to abrowser across the Internet. Webpages are the pages that the user seesin the browser. The Webserver 210 runs software that receives andprocesses requests for webpages from users. The webpages may be storedas files on a storage disk that the Webserver reads and sends to therequesting browser. This is shown by 216. Alternatively, Webserver 210may generate the webpage by gathering information from other sources,such as software programs, and then send it to the browser. For example,Webpages are often generated with data retrieved from an ApplicationServer 230.

The Webserver 210 may be any type of known webserver, such as MicrosoftIIS, or Netscape NES. The architecture shown in FIG. 2 also shows anoptional database 220. The database may be used by the eCommerce system205 to store Internet merchant information, such as customer accountrecords. The database 220 may be any known database type, such asOracle, Sybase, DB2, etc.

In addition, the Internet merchant may have one or more Legacy Systems235. For example, all customer data may be stored on a Legacy System.

In many cases eCommerce systems interact with external systems, as shownby 240. For example, a trading exchange may receive catalogs fromseveral external systems and store them in its own system. It may thenpresent items from the catalogs to interested buyers. The eCommercesystem 205 may communicate with external systems over the Internet orthrough a dedicated Frame Relay Circuit, or any other type of connectionmechanism.

Because Webservers usually do not perform business logic dataprocessing, the architecture typically includes an Application Server230. The Application Server 230 may perform most business specific logicoperations and send data to the Webserver, which processes the data andsends formatted output to the user for display. For example, theApplication Server may retrieve a customer's bank account information,which is used as part of an Order Confirmation webpage generated by theWebserver.

Interprocess communications between the Application Server and theWebserver are typically supported by the underlying operating system.For example, for JAVA based platforms, the communication protocol may beRMI/IIOP (Remote Method Invocation/Internet Inter-ORB Protocol). Theprograms communicating via these methods may or may not reside on thesame physical computer. Similar methods may be used for thecommunications between the Applications Server and the Client Module,which is described below.

Communications between a Legacy System and Application Server may beaccomplished using commercially available software, such as IBM's MQ,Microsoft's MSMQ or Tibco software. The software used depends on theneeds and the underlying operating systems.

The manager's console 265 contains software similar to browser softwarefor displaying output from the inventive system to an employee of theInternet merchant, typically a management-level employee. It is used tomanage the experiments run by the inventive system. It is used toconfigure experiments and display run-time progress data on theexperiment. It may also be used to display data on past experiments.

The client module 250 of the present invention is integrated in theeCommerce system 205. Client Module 250 typically consists of anIntegration Layer 251 and a Client Side Processing module 252.Collectively, it takes as input experiment parameter values and sendsthem to the Server Module 260 for processing. It receives output fromthe Server Module 260, and disseminates the output to the ApplicationServer 230 and/or the Manager's Console 265 for display.

The Client Side Processing Module 252 is responsible for processing allthe input received from the eCommerce System, typically through theApplication Server 230, and delivering it to the Server Module 260. Theinput is typically a continuous stream of parameters used to conduct andmanage an ongoing experiment. The Client Processing Module 252establishes and maintains a secure communication channel with the ServerModule and may also perform session management.

The Integration layer 251 helps the Client Side Processing module 252run on a variety of systems. It acts as an interpreter between theeCommerce System and the Client Side Processing module 252. It may bedifferent for different systems. This enables the Client Side Processingmodule 252 to remain the same, no matter what type of operating systemis being used. In an alternative embodiment, the Client Side Processingmodule may be developed for a specific eCommerce system and runs withoutan Integration Layer.

Communications between the Client Module 250 and the Server Module 260typically use HTTPS to ensure security. Data may also be transmitted inother formats including eXtensible Markup Language (XML) format.

The Dynamic Optimization System 270 includes sub-systems, computers andcommunications systems, including Server Module 260, that are used toperform the sampling and resultant analysis. It receives input data,performs statistical calculations and feeds the output to the eCommercesystem 205. Typically, the output from the Dynamic Optimization System270 is used by the Application Server 230 in performing the businessspecific logic.

Server Module 260 may contain Logic Module 261, Sampling Engine 262 andCommunications Module 263. Server Module 260 is responsible forreceiving input from Client Module 250, performing the experimentationand analysis, and outputting results to Client Module 250. These actionsmay all be performed in a secure environment.

Dynamic Sampling Engine 262 contains statistical functionality that mayperform the various experiments described herein. Logic Module 261contains the algorithms that are used to perform various types ofanalyses on the sampled data.

The Communications module 263 is responsible for securely communicatingdata to and from the Client Module.

Database 275 may be used to store historical data and other dataregarding the experiments for processing, report generation and futureretrieval.

The architecture shown in FIG. 2 is an ASP-based solution, where theServer Module 270 is hosted on a remote system with a network connectionto the eCommerce system 205. In an alternative embodiment, the DynamicOptimization System 270 may reside within the eCommerce system 205.

FIG. 3 illustrates how data may flow through the inventive system. Asshown by entity 360, an operator, who may be a management-level employeefor the Internet merchant, using the inventive system configures theDynamic Optimization System 270 by inputting parameters into the system.For example, the employee may enter a promotional offer range and numberof samples to be used in the experiments. The employee may also activelymonitor the performance of the experiment(s).

These parameters are used as input into the Dynamic Optimization System270 as shown by data 365. These parameters thus configure the samplingengine subsystem of the Dynamic Optimization System 270.

As shown by entity 301, a customer uses a browser to access an eCommercewebsite. When the customer makes a request, several different types ofdata items may be sent to the Webserver, as shown by 305. The Webserverprocesses the information at step 310. If the request from the customerdoes not require Application Server processing, then the Webserver cango ahead and generate the appropriate Webpage, as shown by steps312-315. However, if additional processing is needed, the Webserver willpass on information to the Application Server at step 320. Based on theinformation provided by the Webserver, the Application Server processesthe input and performs any needed calculations at step 325.

During step 325, the Application Server will determine whether it needsthe Dynamic Optimization System 270 to process data. For example, theDynamic Optimization System 270 may process data when there is a currentongoing experiment to determine the optimal price, optimal advertisingcontent, or optimal promotion level, etc.

If the Application Server does not need the Dynamic Optimization System270 to process information, it composes the requested information usinginput from its own calculations, databases and/or legacy systems, asshown by steps 330-335.

Otherwise, the Application Server makes a request to the DynamicOptimization System 270 and passes on any information required by theDynamic Optimization System 270 for performing the statisticalcalculations, as shown by step 340.

The Dynamic Optimization System 270 may use historical data in itscalculations as shown by data 350. In addition, the parameters 365entered by the Internet Merchant are used in the calculations that theDynamic Optimization System 270 performs.

The Dynamic Optimization System 270 performs the calculations asrequired, and outputs the resulting data at step 345. The ApplicationServer composes the requested information at step 335 using the outputfrom 345.

If the manager is actively monitoring the progress of the experiment, hewill be informed of the progress as shown by steps 370-360.

The sampling engine 262 of the Dynamic Optimization System 270 may beused by many different applications to obtain information about currentmarket conditions. These applications use the sampling data to determineoptimal pricing, promotions level, promotion timing, product bundling,lead time discounts, quantity discounts, price versus financing and typeand content of banner ads, for example. The Logic Module contains thealgorithms to perform the different types of analyses required bydifferent applications. Other applications of the sampling data will beknown to those skilled in the art and are intended to come within thescope of the present invention.

The dynamic sampling engine may be considered the core of the inventivesystem. As shown by FIG. 1, it can be translated into modules forpricing, promotions, product bundling, yield management, lead timediscounts, quantity discounts, price versus financing and banneradvertisement content.

A promotion module is described below.

Dynamic Promotion to Mitigate Customer Defection or Attrition

It is easy to change eCommerce promotions by simply updating a Web page.In addition, it is possible to present different promotions to differentonline customers without either customer learning the promotion that hasbeen offered to the other. This may be accomplished by presentingdifferent levels of promotion to different potential customers, forexample. Because of these reasons, it is possible to perform controlled,real-time experiments on samples of the customer population to determinecustomer promotion sensitivities. This information can then be used todetermine real-time optimal promotion strategies for an entire customerpopulation or for selected segments of the customer population. Inaddition, merchants may learn from the online experiments, and applythis learning to offline counterpart market strategies.

The sampling experiments conducted by the method and system of thepresent invention are designed to measure different customerinclinations. For example, one area of measurement may be to measurecustomer inclination to purchase a product at differing promotionallevels. In this application, the amount of the promotion is deliberatelyvaried by the inventive system during a sampling period, and statisticsare kept by the system to determine what percentage of customers arelikely to buy or exhibit interest in the product at the differentpromotional levels. The statistics typically include, for example, thenumber of customers who actually purchased the product at eachpromotional level.

Given the percentage of customers who buy, or who exhibit a quantifiableinterest in, the product at each promotional level, the system is ableto compute an optimal or near-optimal promotional level for the product.The optimal promotional level determined by the system is intended tooptimize an economic variable, such as customer retention or profit. Theeconomic variable to be optimized may be financial, such as profit orrevenue. Alternatively, the economic variable may be another quantity ofinterest, such as market share, customer satisfaction, customerretention at the website, or utilization of manufacturing or shippingresources, for example. The optimization typically determines thepromotion at which an economic variable is maximized, although othertypes of optimization, such as minimizing an economic variable, arepossible using the method and system of the present invention.

In one embodiment, the objective function may weigh multiple criterions.For example, the user may be trying to optimize both profit and marketshare. The objective function may be defined to be 75% weighted towardprofit optimization and 25% toward customer retention. The inventivesystem in this case will determine which price optimizes this weightedmulti-criterion function.

The dynamic promotional level application allows companies to determineoptimal promotional level by running continuous real-time models on anappropriate sample population, which may be determined automatically bythe sampling engine.

The present invention operates on the concept that the likelihood that acustomer will return to a website decreases as the elapsed time from thelast access of that website by that customer increases. Thus, thepresent invention includes a method and system of determining thelikelihood that a customer will not return to a website and a method andsystem of providing an incentive to that customer to return to thewebsite.

To determine the likelihood that a customer will not return to a websitebased on the length of time since that customer last accessed ortransacted with the site, the present invention may perform astatistical analysis of past customer access frequency and thelikelihood of retaining those customers based on that access frequency.Managers of the firm may explicitly state a time interval by which if acustomer had failed to interact with the site, the customer is deemed tobe defunct, wherein “defunct” indicates that the client is not expectedto return to the web site. The term “interaction” may be defined as avisit to the site or purchase of an item or service from the site.(Based on historical data, the probability of defection conditional onlapsed time since last interaction can be calculated by determining thepercentage of customers that return to the site after having nointeraction with the site for a selected time period.) Typically theprobability that a customer will return decreases as the lapse timeincreases.

In an example where a web merchant considers any customer who has notinteracted with the site for six or more months to be defunct. Based onhistorical data, the probability that a customer becomes defunct whenthe customer has not interacted for one month may be, for example, 0.3.The probability of the customer becoming defunct rises to 0.6 by the endof three months.

Furthermore in this example, the merchant may select that when aprobability of the customer becoming defunct is equal to 0.6, athreshold has been surpassed. Henceforth, that threshold will bereferred to as a “critical threshold.” Thus, the merchant may decidethat when the likelihood of a customer not returning to interact withthe site exceeds a 0.6 likelihood, the merchant will take steps toretain that customer rather than permit the customer to become defunct.When a customer, therefore, has not interacted with the web site for aperiod of time corresponding to the time when historically only 0.6, or60% of customers will return to interact with the web site, the merchantmay offer a promotion to the customer to attract the customer back tothe site to interact.

In a certain embodiment of the present invention, a method of providingan incentive to that customer to return to the website is provided. Thatmethod uses the critical threshold, which may have been determined by auser, and the defunct probability obtained through the previouslydescribed analysis of historical data. The method minimizes the cost ofcustomer retention.

In one embodiment of the present invention, a customer that has notaccessed the site within the defunct threshold period is proactivelysought out and provided with an incentive to access the site and make apurchase therefrom. In that embodiment, the customer may be provided anelectronic coupon by way, for example, of an email inviting the customerto accept a discount on a purchase made by the customer. Like anycoupon, the electronic coupon may be redeemable only for selected goodsor services and may be valid for a limited period of time.

In a certain embodiment of the present invention, the promotion level,or amount of discount offered in a coupon is sampled. Such sampling maybe conducted by offering different amounts of discounts to variousrandom samples of customers whose defunct probability exceeds thethresholds. For example, managers may consider a promotional discountfrom six to eight dollars for all customers with a defunct probabilityof 0.6 or higher. Sampling increments may then be determined. Forexample, if a sampling increment of one dollar is desired, discounts ofsix dollars, seven dollars, and eight dollars may be offered to threerandom samples of customers exceeding the threshold. Alternately, if asampling increment of fifty cents is desired, discounts of six dollars,six dollars and fifty cents, seven dollars, seven dollars and fiftycents, and eight dollars may be offered to five different groups ofcustomers.

The size of the random sample is determined based on the manager'slevels of confidence intervals. These methods are known to those skilledin the art. Furthermore, the following statistical references areincorporated by reference in their entirety: (a) Ross (1997), A FirstCourse in Probability, Prentice Hall, Upper Saddle River, N.J.; (b)Gelman A., J. B. Carlin, H. S. Stern and D. B. Rubin (1995), BayesianData Analysis, Chapman & Hall, New York, N.Y.; (c) Malhotra, N. K.(1993), Marketing Research, Prentice Hall, Englewood Cliffs, N.J.; (d)Wedel, M and W. A. Kamakura (1998), Market Segmentation: Conceptual andMethodological Foundations, Kluwer Academic Publishers, Boston, Mass.;(e) Pudney (1989), Modeling Individual Choice: The Econometrics ofCorners, Kinks and Holes, Basil Blackwell Limited, Oxford, UnitedKingdom; (f) Cinclair E. (1975), Introduction to Stochastic Processes,Prentice-Hall, Englewood Cliffs, N.J.; (g) Kalbfleisch, J. D. and R. L.Prentice, The Statistical Analysis of Failure Time Data, John Wiley &Sons, New York, N.Y.; and (h) Mitchell, T. M (1997), Machine Learning,McGraw-Hill, New York, N.Y. Those references describe statisticalmethods that may be utilized by the present invention.

FIG. 4 illustrates a method of optimizing customer retention. At 410,the operator of the present invention determines whether he isinterested in maximizing customer retention or maximizing profit byproviding customers who are likely to become defunct with an incentive,such as a promotion, to return to interact with the web site. Theoperator specifies a permissible defunct threshold over which the lossof customers is unacceptably high. The operator may define defunctthresholds by segment where necessary. For example, the acceptabledefunct threshold for customers purchasing more than $1000 from the sitein the past may be lower than the acceptable defunct threshold forcustomers purchasing more than $50 from the site in the past. Thus, thesample may be divided by segments having different defunct thresholds.The operator will also specify the level, range and interval of thepromotion to be offered for each segment.

At 415, the operator uses historical data to identify the probabilitythat a customer will become defunct, or exit as a customer, for eachsegment to be sampled at a particular time since the customer lastinteracted with the site.

At 420, the operator determines whether the probability that an existingcustomer or group of customers will become defunct exceeds the desireddefunct threshold based on the amount of time that has lapsed sincetheir last interaction with the site. If the probability that thecustomer or group of customers are likely to have become defunct doesnot exceed the defunct threshold, then the present invention will waitan additional period of time at 423 and save data related to whetherthose customers interact with the web site during that time period at425. If a customer does interact during that time, then there is no needto provide an incentive for that customer to return. Thus, the systemwill drop a customer who returns to interact from that sample at 428.That customer may be included in future iterations of the presentinvention; however, the time since last interaction will begin to tollonce again from the most recent interaction time.

When a customer does not return to the site after the additional waitingperiod, the present invention will once again determine the likelihoodthat the customer will not return at 415.

When the defunct threshold is exceeded at 420, that customer may bepassed to the dynamic sampling engine 262 described hereinbefore for adetermination of the optimum promotional level to be offered.Experimentation utilizing the dynamic sampling engine 262 may berepeated periodically to ensure that the optimal promotion isdynamically optimized to regularly compensate for market changes. Thus,experiments utilizing the dynamic sampling engine 262 may be runmonthly, weekly, daily, hourly, or more often, until the experimentationbecomes, practically speaking, continuous. Dynamic optimization,therefore, is a result of continuous experimentation. The optimumpromotion may, furthermore, be propagated to the web at 435 for offeringto customers each time a new optimum promotion level is discovered bythe dynamic sampling engine. Alternately either the system or theoperator may propagate the optimum promotion each time the optimumpromotion level changes by a particular amount from the previouspromotion level such as, for example, $0.25. Data from the web 432, suchas purchase, timing, and use of promotions by customers may also beprovided from the web 435 to the dynamic sampling engine for use infuture samples.

The result of the experimentation performed by the dynamic samplingengine is an indication of the current probability that a customer willexit the site and not return to the site conditioned on the lapse timesince the last interaction by the customer and the amount of thepromotion offered at 440. At 445, the process 400 ends, however, theprocess 400 may be repeated at regular intervals.

As an example of the operation of the dynamic sampling engine, let“CPRj” be the Cost of Promotion for product j (“PRj”). Let the randomsample for each promotion be a constant “m”. Let “fj” be the fraction ofsample customers who accept the promotion in the defined time andinteract with the site. Let “V” be the lifetime value or expected profitto be made from the returning customer calculated from historical data.Based on the experimental information, the objective is to maximizeprofit which may be defined by the equation: Maximum Profit for ProductPRj is equal to the number of customers sampled times the fraction ofcustomers who accept the promotion times the difference between thenormal profit made from the product less the profit lost through thepromotion or mfj(PRj-CPRj).

It is noted that the profit lost through the promotion may not be theentire amount offered but, rather, may be equal to the ratio of cost tosales price. Thus, a promotion of $8 will include a lost profit of $6.40where cost of the product is 80% of the sales price.

Taking a specific example, consider promotions of six, seven and eightdollars. The costs of the promotions to the firms are $4.8, $5.6 and$6.4, respectively. Given the constant sample size of 1000 for eachpromotion, the experiment reveals that 0.3, 0.4 and 0.41 fractions ofthe samples respond to the promotions. The lifetime value or profit of areturning customer is $25 which has been obtained from historical data.Therefore, the profit for various promotions are as follows: the profitof a promotion of $6=(1000×0.3×[25−4.8])=$6060, the profit of apromotion of $7=1000×0.4×[25−5.6]=$7760; and the profit of a promotionof $8=1000×0.41[25−6.4]=7626, therefore, the optimal promotion is at the$7 level because that level offers the highest profit.

The Internet merchant may also determine the customer population. In oneembodiment, the population may include every potential customer thatvisits the web site. Alternatively, the customer population may beclustered or segmented, and only customers who meet a certain profileare sampled. As an example, customers may be clustered intosocioeconomic groups, and only customers in certain groups are sampledwhen determining an optimal price. Alternatively, the entire customerpopulation may be segmented, with separate experiments run on eachsegment determining an optimal price for each segment. As anotherexample, customers may be identified for sampling based upon purchasinghistory or other accumulated data. For example, the segmentation schememay cluster customers based on purchase history: heavy buyers, lightbuyers and non-buyers. Segments may be determined from a combination ofdemographic variables and prior purchase histories.

The result of utilizing that method is a determination of how long towait before offering a discount in order to retain a customer and whatthe optimal promotional offer is.

The representative may then input promotion amounts to be sampled. Thosesample promotion amounts typically include the current promotion amountbeing offered, and a number of specified optional promotion amounts bothabove and below the current promotion amount. Preferably, a sufficientlylarge number of promotion amount points are tested so that there areenough promotion amount points to determine a smooth curve in the profitfunction. In an alternative embodiment, the representative may enter arange of promotional amounts to be sampled, and sampling intervals andthe system may determine individual promotional amounts to be sampled inthat range at specified intervals. The optimal promotion may beautomatically propagated to the entire population of customers whoexceed the defunct probability.

The option should clearly state the objective function. The objectivefunction, for example, can be maximized customer attention or, as in thenumerical example we presented hereinbefore, it could be maximum profitfrom defunct customers. It is also important for the managers to specifythe exit-probability thresholds. For example, they might determine thatif the threshold probability is 0.3, that means that any time theprobability of defunct exceeds 0.3, they should immediately adopt morepromotions so that they can pull the defunct customers back and attractthem to the site.

The probabilities might furthermore change by the segments. For example,large important customers might have thresholds of 0.3, while lessimportant people who buy less could have a threshold of 0.5. Thus, theoperator maybe willing to accept a higher probability of exit withcustomers who buy less because these customers are not so important. Thethreshold could be based on purchase history, that is somebody thatbought a lot in the past is more important and the operator may wish toaccept a threshold of 0.3. Others buy less, so the operator may bewilling to accept a high threshold of 0.5. Another sample could be basedon pure segmentation. For example, high-income customers may beimportant because they are potentially highly profitable, therefore,operators accept a very low threshold of their exit. An operator mayspecify this for each segment if they are doing a segment basisanalysis. The third item that they may specify is the range ofpromotion, which in this example, will be $6 through $8. Next, theoperator will determine the intervals. Those intervals may, for example,be in $1 increments such that the samples would include $6, $7, and $8.Alternately, the intervals may be in $0.5 increments providing samplesof $6, $6.50, $7, $7.50 and, $8. They may also specify the desiredlevels of confidence intervals. The next step is to use the historicaldata to identify the probability of exit or the probability of acustomer becoming defunct and not returning to the site. That may bedone on a segment-by-segment basis based on the historical data. Forexample, for a given customer at a point in time, a determination may bemade as to whether the exit or defunct probability exceeds the desiredthreshold. Again, taking the example of a customer for whom the defunctprobability is 0.3, if that is the threshold and it has reached 0.3,then we move on to perform dynamic sampling. Dynamic sampling is theprocess by which various promotions are offered to various subsamples ofcustomers as we denoted in the previous experimental example. We mayalso find the optimum promotion based on maximizing the profit fromdefunct customers. In the interactive site given to the operators, weshow what the historical probabilities of exit that we have calculatedare and then we indicate what fraction of customers respond to variouspromotions and how the profit changes at various promotion levelsindicating what the optimal profit is. The optimal promotion may then beautomatically propagated to the website. Suppose the probability doesnot exceed the threshold so we would wait for one more unit period oftime. There is a possibility that the customer could have beeninteracted at that point of time because we are waiting for anadditional amount of time. We may then ask whether the customerinteracted. If the customer has interacted, we know the customer hasreturned and we will not have to worry about that customer. If thecustomer did not interact, then we have to again go back and ask what isthe probability of that customer being defunct now and return to adetermination of whether the threshold has been exceeded.

In another embodiment, the present invention monitors the Internetinteraction between a selling organization and its customers based on avariety of indicators of performance. These indicators are used topredict when the buyer/seller relationship is degrading and a defectionof the customer has become more likely so the seller may take measuresto prevent such defection.

The present invention comprises a computer system that collects dataabout customers who leave a website prior to obtaining service in aneffort to develop a profile of such customers so that later customerswho visit the site and conform to the profile can be served morequickly. Such favored treatment is possible on the Internet because thevarious individuals who are waiting for service at a website are unknownand invisible to one another. In a real physical store it would beundesirable to serve customers out of the order in which they arrivedbecause of the risk of offending customers not so chosen.

The quality of a buyer/seller relationship can be measured in many ways,some of which are specific to particular industries. For example, therate at which a customer places orders, either in number of orders permonth or dollar volume per month, is such an indication. A customerwhose purchases are tailing off may have become dissatisfied with thegoods or the supplier and may cease purchasing altogether in the future.A customer whose rate of purchasing is tailing off may likewise beconsidering defection. In some cases more subtle information may have tobe considered. An increase in the volume of complaints about productquality from the same customer may suggest that the relationship istroubled.

In many cases, the supplier has the ability to retain the customer priorto defection if the potential for defection is noted and the cause canbe determined. The supplier may then take steps to preserve therelationship. In the Internet world, however, the number of customersmay be huge (millions or tens of millions) and the average order may besmall (tens of dollars). It is impractical to have human beings monitorsuch a large number of interactions on an individual basis. This processmust be automated.

The present invention monitors variables believed by the seller to beindicative of potential defection, performs statistical analysis todetermine whether the observed variables are within normal statisticalvariance or suggest potential defection, and alerts the seller of such apossibility.

The invention initially performs sampling to gauge the impatience ortendency to balk over time, of the space of visitors and segments themby chosen variables. For example, visitors deemed to be wealthy may bemore impatient than other visitors, but can be expected to spend moremoney at a site. Once a visitor is known to be wealthy, by beingidentified or by having his worth inferred, his impatience can beestimated from the statistical model built by the invention.

When subsequent visitors arrive at the site, estimates of theirimpatience or the expected profit to be obtained from them as theservice time increases can be estimated. The assignment of customers toqueues and their ordering within queues can be adjusted to maximize anobjective function.

Various different objective functions may be used. One is to minimizethe number of visitors who will balk. By estimating the expected servicetime necessary to serve a particular impatient visitor, it is possibleto estimate the number of queued visitors who will leave the site priorto being served. If the impatience of the fourth visitor in the queue isknown, then the probability that the fourth visitor will balk can becomputed.

For example, the time between logins (“inter-login time”) is a randomvariable thought to be closely related to defection. In fact, aninfinite inter-login time is the very indicator of a permanentdefection. However, even a dedicated regular customer may exhibitsignificant variance in inter-login times due to vacations, businessseasonality, short-term concerns, and the like. It is unrealistic totreat every rise in inter-login time as a potential defection. What isimportant is how the recent distribution and sequence of inter-logintimes compares to the previous or expected behavior of the customer.

The method of the present invention comprises:

1. Collecting information about visitors to a website, includinginformation about the length of time said visitors remain at the sitewithout receiving service.

2. Segmenting said visitors by impatience.

3. In a website that maintains service queues, determine of each visitora degree of impatience based on a segment to which he belongs.

4. In said website, assign said visitor to a service queue and to asuitable position within said queue to maximize a given objectivefunction.

In an improvement of the present invention, a visitor who is about to beforced to wait beyond the time when he can be expected to balk mayautomatically be offered a premium, such as a discount on a product or acredit toward future purchases, in return for his willingness to remainin the queue.

The visitor segmentation and estimates produced by the data can besubjected to constant dynamic update based on actual measurements ofbalking time.

It is in general not possible to determine when a visitor “leaves” awebsite. This is a result of the stateless nature of the HypertextTransfer Protocol (HTML—Hypertext Mark-up Language) used to request anddeliver web pages, under which there is no direct connection between thevisitor and the site. The visitor occasionally requests a web page andthe site delivers it. What the user is doing between requests is notknown to the site. Therefore, it is not a well-defined event for a userto “leave.”

For the purposes of the present invention, a visitor will be deemed tohave “left” a website when he has failed to request a new page or hasnot sent data to the site for a defined period of time. Said time periodmay be set by the user of the invention.

The problem of determining when a visitor leaves a site will becomeeasier with subsequent engineering changes to the Internet. Laterversions of the Internet Protocol will establish an express link betweenclient and server that will facilitate determining when a visitor has“left” a website.

It is possible to trap and foil efforts to leave a website in favor ofanother. The visitor can then be required to click a virtual button toindicate that he wants to exit the site, which will give a positiveindication of such an event. Even with this expedient, however, it isnot possible to detect when the user has moved to another window on acomputer display or whether he is simply absent from the room in whichthe computer is located. To do this one may combine the technique offorcing an express exit click and measuring the time between clicks toinfer whether a visitor has “exited.”

In a preferred embodiment, an Internet computer system for interactingwith customers (e.g. a web server serving content and affording anopportunity to place orders) already exists. The present invention isused as add-on software that connects to the pre-existing system tomonitor interactions.

In a further embodiment, the system is provided with training data(either in advance or as the system is being used) indicating whether aparticular customer in fact defected. The system can then use prior artmethods, such as neural networks, to train the system to recognizepatterns similar to those in the training data and thereby sharpen itspredicative ability.

In said embodiment, the computer method of the present invention is asfollows:

1. Provide the system with training data indicating previous defectionsand the values of the indicator variables for each such case.

2. Train the system on the training data to predict probability of nextpurchase and probability of defection based on the time sequence ofindicator variables.

3. Observe the indicator variable for each identified customer inreal-time.

4. If the defection probability is outside control levels, alert theseller to the possibility of defection.

In a further embodiment, automatic remedial action may be taken when thedefection probability exceeds a defined level. For example, the customermay be offered a discount or other promotion, or email may be sent. Thiscan be done without human intervention or involvement. The effectivenessof the automated methods may themselves be monitored and the parametersaltered dynamically based on actual experience.

In a further embodiment, the customers are segmented via clusteringtechniques into groups whose correlation between indicator variables anddefections are similar. The system then attempts to categorize eachcustomer by the segment to which it belongs. In this manner thedefection characteristics of the segmented group can be used to predictdefection by an individual, in the absence of detailed information abouta specific customer. Segmentation may be done by past purchase history,payment history, geographic location, socioeconomic status, or any othermethod that results in meaningful clusters.

In a further embodiment, the seller is provided with a graphic interfacefor real-time monitoring of its web activities, impending defections(either on a case-by-case or aggregated statistical basis), andeffectiveness of defection countermeasures.

It is a benefit of the present invention that Internet customerdefection can be detected without human intervention or attention.

It is a benefit of the present invention that the impatient behavior ofvisitors to a website can be elicited through experimental sampling.

It is a benefit of the present invention that the general level ofsatisfaction of visitors to a website can be increased by reducing thetime required to service impatient individuals but without the need toreduce average service times.

It is a benefit of the present invention that managers can be providedwith actual data concerning the impatient behavior of website visitors.

It is a further benefit of the present invention that measurement ofimpatience and resulting queuing adjustments can be made continuouslyand, therefore, a website can respond to changing usage and visitorpopulation patterns.

It is a further benefit of the invention that detected Internet customerdefections can be prevented without human intervention or attention.

It should be apparent that references to the Internet only comprise asubset of the potential embodiments of the present invention and thatall that is required is some means for monitoring the relevant variableindicative of defection so that potential defections can be anticipatedand dealt with. Said monitoring means may include, but are not limitedto, private leased networks, local area networks, wide area networks,cable television systems, cellular telephone systems, wirelesscommunication systems, infrared systems, and satellite systems.

While the invention has been described in detail and with reference tospecific embodiments thereof, it will be apparent to one skilled in theart that various changes and modifications can be made therein withoutdeparting from the spirit and scope thereof. Thus, it is intended thatthe present invention cover the modifications and variations of thisinvention provided they come within the scope of the appended claims andtheir equivalents.

What is claimed is:
 1. A method of dynamically optimizing customerretention for a web marketing site, comprising: specifying a permissibledefunct threshold; specifying a range of offers to be included in a setof promotions; determining a probability that a customer will becomedefunct in a predetermined period of time since the last interaction ofthat customer with the web site; and providing a promotion to a customerif the probability that the customer will become defunct in thepredetermined period of time since the last interaction of that customerwith the web site is greater than a predetermined threshold.
 2. Themethod of claim 1, further comprising segmenting the sample populationbased on a characteristic of the customers sampled.
 3. The method ofclaim 2, wherein the characteristic is an amount that the customersspent at the web site in the past.
 4. The method of claim 1, wherein thelast interaction includes accessing the web site.
 5. The method of claim1, wherein the last interaction includes making a purchase from the website.
 6. The method of claim 1, further comprising maximizing profit byoptimizing an amount of discount offered in the promotion.
 7. The methodof claim 6, wherein optimizing is performed continuously.
 8. The methodof claim 6, wherein optimizing includes sampling responses received fromcustomers that are offered promotions of varying amounts; and optimizingthe promotion amount provided to other customers based on the optimumpromotion amount discovered in the sample.
 9. The method of claim 1,wherein data related to whether a customer has interfaced with the website is stored in the database.
 10. The method of claim 1, wherein anamount spent by a customer is stored in a database.
 11. The method ofclaim 9, wherein a customer is segmented for random sampling based onthe amount spent by that customer.